Remove keras dependency from TFLite tests and BUILD files

PiperOrigin-RevId: 340033160
Change-Id: Ia7007ddb1bcd128c684712eafa284af79fedac05

tensorflow/lite/build_def.bzl

@@ -763,6 +763,7 @@ def gen_model_coverage_test(src, model_name, data, failure_type, tags, size = "m
                 "no_windows",
             ] + tags + coverage_tags,
             deps = [
+                "//third_party/py/tensorflow",
                 "//tensorflow/lite/testing/model_coverage:model_coverage_lib",
                 "//tensorflow/lite/python:lite",
                 "//tensorflow/python:client_testlib",

tensorflow/lite/python/BUILD

@@ -125,7 +125,6 @@ py_test(
         "//tensorflow/python:session",
         "//tensorflow/python:tf2",
         "//tensorflow/python/eager:def_function",
-        "//tensorflow/python/keras",
         "//tensorflow/python/saved_model",
         "//tensorflow/python/saved_model:save",
         "//tensorflow/python/training:training_util",
@@ -151,7 +150,6 @@ py_library(
         "//tensorflow/lite/experimental/tensorboard:ops_util",
         "//tensorflow/lite/python/optimize:calibrator",
         "//tensorflow/python:graph_util",
-        "//tensorflow/python/keras",
         "//tensorflow/python/saved_model:constants",
         "//tensorflow/python/saved_model:loader",
         "@six_archive//:six",
@@ -170,6 +168,7 @@ py_test(
     ],
     deps = [
         ":lite",
+        "//tensorflow:tensorflow_py",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_test_lib",
         "@six_archive//:six",
@@ -392,7 +391,6 @@ py_test(
         "//tensorflow/python:nn",
         "//tensorflow/python:platform_test",
         "//tensorflow/python:session",
-        "//tensorflow/python/keras",
         "//tensorflow/python/ops/losses",
         "//tensorflow/python/saved_model",
     ],

tensorflow/lite/python/lite_test.py

@@ -28,13 +28,13 @@ from absl.testing import parameterized
 import numpy as np
 import six
 from six.moves import range
+from tensorflow import keras
 
 from tensorflow.lite.python import lite
 from tensorflow.lite.python import lite_constants
 from tensorflow.lite.python.convert import ConverterError
 from tensorflow.lite.python.convert import mlir_quantize
 from tensorflow.lite.python.interpreter import Interpreter
-from tensorflow.python import keras
 from tensorflow.python.client import session
 from tensorflow.python.eager import context
 from tensorflow.python.eager import def_function

tensorflow/lite/python/lite_v2_test.py

@@ -35,8 +35,6 @@ from tensorflow.lite.toco import types_pb2 as _types_pb2
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import test_util
-from tensorflow.python.keras.layers import recurrent
-from tensorflow.python.keras.layers import recurrent_v2
 from tensorflow.python.lib.io import file_io
 from tensorflow.python.platform import test
 from tensorflow.python.saved_model import save_options
@@ -1147,9 +1145,9 @@ class ControlFlowTest(lite_v2_test_util.ModelTest):
         expected = expected.c
       self.assertAllClose(expected, actual)
 
-  @parameterized.named_parameters(('LSTM', recurrent_v2.LSTM),
-                                  ('SimpleRNN', recurrent.SimpleRNN),
-                                  ('GRU', recurrent_v2.GRU))
+  @parameterized.named_parameters(('LSTM', tf.keras.layers.LSTM),
+                                  ('SimpleRNN', tf.keras.layers.SimpleRNN),
+                                  ('GRU', tf.keras.layers.GRU))
   @test_util.run_v2_only
   def testKerasRNN(self, rnn_layer):
     # This relies on TFLiteConverter to rewrite unknown batch size to 1. The
@@ -1171,9 +1169,9 @@ class ControlFlowTest(lite_v2_test_util.ModelTest):
     expected_value = model.predict(input_data)
     self.assertAllClose(expected_value, actual_value, atol=1e-05)
 
-  @parameterized.named_parameters(('LSTM', recurrent_v2.LSTM),
-                                  ('SimpleRNN', recurrent.SimpleRNN),
-                                  ('GRU', recurrent_v2.GRU))
+  @parameterized.named_parameters(('LSTM', tf.keras.layers.LSTM),
+                                  ('SimpleRNN', tf.keras.layers.SimpleRNN),
+                                  ('GRU', tf.keras.layers.GRU))
   @test_util.run_v2_only
   def testKerasRNNMultiBatches(self, rnn_layer):
     input_data = tf.constant(
@@ -1200,7 +1198,7 @@ class ControlFlowTest(lite_v2_test_util.ModelTest):
     model.add(tf.keras.layers.Input(batch_size=1, shape=(10, 10), name='input'))
     model.add(
         tf.keras.layers.Bidirectional(
-            recurrent_v2.LSTM(units=10, return_sequences=True),
+            tf.keras.layers.LSTM(units=10, return_sequences=True),
             input_shape=(10, 10)))
     model.add(tf.keras.layers.Flatten())
     model.add(tf.keras.layers.Dense(5))
@@ -1221,7 +1219,7 @@ class ControlFlowTest(lite_v2_test_util.ModelTest):
         np.array(np.random.random_sample((1, 10, 10)), dtype=np.float32))
     model = tf.keras.models.Sequential()
     model.add(tf.keras.layers.Input(batch_size=1, shape=(10, 10), name='input'))
-    model.add(tf.keras.layers.Bidirectional(recurrent_v2.LSTM(units=10)))
+    model.add(tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(units=10)))
     model.add(tf.keras.layers.Dense(5))
     model.add(tf.keras.layers.Activation('softmax'))
 

tensorflow/lite/testing/model_coverage/BUILD

@@ -10,6 +10,7 @@ py_library(
     srcs_version = "PY2AND3",
     tags = ["no_pip"],
     deps = [
+        "//tensorflow:tensorflow_py",
         "//tensorflow/lite/python:lite",
         "//tensorflow/python:platform",
     ],

tensorflow/lite/testing/model_coverage/model_coverage_lib.py

@@ -22,6 +22,7 @@ import os
 
 import numpy as np
 from six import PY2
+from tensorflow import keras
 
 from google.protobuf import text_format as _text_format
 from google.protobuf.message import DecodeError
@@ -29,13 +30,11 @@ from tensorflow.core.framework import graph_pb2 as _graph_pb2
 from tensorflow.lite.python import convert_saved_model as _convert_saved_model
 from tensorflow.lite.python import lite as _lite
 from tensorflow.lite.python import util as _util
-from tensorflow.python import keras as _keras
 from tensorflow.python.client import session as _session
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.framework.importer import import_graph_def as _import_graph_def
-from tensorflow.python.keras.preprocessing import image
 from tensorflow.python.lib.io import file_io as _file_io
 from tensorflow.python.platform import resource_loader as _resource_loader
 from tensorflow.python.saved_model import load as _load
@@ -71,8 +70,9 @@ def get_image(size):
   """
   img_filename = _resource_loader.get_path_to_datafile(
       "testdata/grace_hopper.jpg")
-  img = image.load_img(img_filename, target_size=(size, size))
-  img_array = image.img_to_array(img)
+  img = keras.preprocessing.image.load_img(
+      img_filename, target_size=(size, size))
+  img_array = keras.preprocessing.image.img_to_array(img)
   img_array = np.expand_dims(img_array, axis=0)
   return img_array
 
@@ -342,7 +342,7 @@ def evaluate_keras_model(filename):
   Returns:
     Lambda function ([np.ndarray data] : [np.ndarray result]).
   """
-  keras_model = _keras.models.load_model(filename)
+  keras_model = keras.models.load_model(filename)
   return lambda input_data: [keras_model.predict(input_data)]
 
 
@@ -740,7 +740,7 @@ def test_keras_model_v2(filename,
       (half-inclusive). (default None)
     **kwargs: Additional arguments to be passed into the converter.
   """
-  keras_model = _keras.models.load_model(filename)
+  keras_model = keras.models.load_model(filename)
   if input_shapes:
     for tensor, shape in zip(keras_model.inputs, input_shapes):
       tensor.set_shape(shape)

tensorflow/lite/testing/model_coverage/model_coverage_lib_test.py

@@ -22,10 +22,10 @@ import os
 import tempfile
 
 import numpy as np
+from tensorflow import keras
 
 from tensorflow.lite.python import lite
 from tensorflow.lite.testing.model_coverage import model_coverage_lib as model_coverage
-from tensorflow.python import keras
 from tensorflow.python.client import session
 from tensorflow.python.eager import def_function
 from tensorflow.python.framework import constant_op